CN108061555B - Vehicle positioning error correction method and device - Google Patents

Vehicle positioning error correction method and device Download PDF

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CN108061555B
CN108061555B CN201610988802.7A CN201610988802A CN108061555B CN 108061555 B CN108061555 B CN 108061555B CN 201610988802 A CN201610988802 A CN 201610988802A CN 108061555 B CN108061555 B CN 108061555B
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vehicle
prediction
prediction curve
module
center line
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CN108061555A (en
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孙忠平
于欢
宋永刚
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Huawei Technologies Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30—Map- or contour-matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/40—Correcting position, velocity or attitude
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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  • Automation & Control Theory (AREA)
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Abstract

The invention discloses a vehicle positioning error correction method and device, which are used for solving the problem that the correction accuracy of delayed positioning errors is larger due to larger difference between a predicted track curve and the actual trend of a vehicle in the prior art. The method specifically comprises the following steps: acquiring vehicle state information and a vehicle running center line, and estimating a longitudinal distance of the vehicle, a transverse deviation of a position of the vehicle from the center line, and an arc length value of a closest point on the center line to the position of the vehicle; acquiring predetermined N offset distances; determining an ith prediction curve according to the ith offset distance, the longitudinal distance, the transverse deviation, the arc length value and the vehicle state information in the N offset distances; selecting one prediction curve from the determined N prediction curves as a target prediction curve; and determining the position reached by the vehicle after running along the target prediction curve at the running speed in the state information for a preset delay time by taking the position of the currently detected vehicle as a starting point.

Description

Vehicle positioning error correction method and device
Technical Field
The invention relates to the technical field of vehicle positioning, in particular to a vehicle positioning error correction method and device.
Background
The current automatic driving of the bicycle mainly comprises three modules of perception, planning decision and control. The planning decision is to generate a motion track according to the sensed real-time road condition, and then enable the vehicle to finish the actions of keeping a lane, avoiding obstacles and the like according to the generated motion track.
With the development of the automatic driving technology of the single vehicle, the network must be combined with the automatic driving technology to develop a network-side control core. The network end plans a four-dimensional path which accords with the non-overlapping space-time according to the real-time position, speed and course and other state information of the vehicle group so as to complete the actions of multi-vehicle cooperation and the like.
The network end has high requirements on the real-time performance and accuracy of position and course information when planning a path, but due to the problems of network instability and time delay, the positioning error is greatly increased, thereby causing great difficulty for the realization of applications such as multi-vehicle cooperation and the like.
The first method is to predict the future track of the vehicle by using a least square curve fitting method according to the accumulation of a plurality of points included in the existing track so as to obtain a predicted track curve, thereby carrying out error correction. However, in the first method, when the moving direction of the vehicle changes greatly, the difference between the predicted trajectory curve and the actual vehicle trend is large. And the second method is to match the current vehicle position with a map, so as to obtain a center line which is close to the current vehicle position and reflects the future trend of the vehicle from the center lines corresponding to a plurality of lanes on the map as a predicted track line, and further carry out error correction. For the second, the movement locus of the vehicle may not completely coincide with the lane center line, and therefore the center line as the predicted trajectory line is caused to be greatly different from the actual running direction of the vehicle.
Disclosure of Invention
The embodiment of the invention provides a vehicle positioning error correction method and device, which are used for solving the problem that the correction accuracy of delayed positioning errors is high due to the fact that the difference between a predicted track curve and the actual trend of a vehicle is large in the prior art.
In a first aspect, an embodiment of the present invention provides a method for correcting a vehicle positioning error, where the method is applicable to an electronic device, where the electronic device may be a cloud server or a vehicle-mounted terminal, and the method includes:
the method comprises the steps that the electronic equipment obtains state information of a vehicle and a center line of vehicle running; the state information comprises the position, the course angle and the running speed of the vehicle which are detected currently; the center line of the vehicle running is the center line of the preset length on the lane where the vehicle is located at present and from the position; the course angle is an included angle between the driving direction of the vehicle and the positive north direction. Then, the electronic equipment estimates the longitudinal distance of the vehicle advancing based on the running speed of the vehicle included in the state information of the vehicle, determines the transverse deviation of the position of the vehicle from the central line of the vehicle according to the state information of the vehicle and the central line of the vehicle, and determines the arc length value of the closest point on the central line to the position of the vehicle. Then the electronic equipment acquires N predetermined offset distances, wherein the ith offset distance is the estimated transverse deviation of the ith position of the vehicle deviating from the driving center line of the vehicle, i is a positive integer not larger than N, and N is a positive integer larger than 1; then the electronic equipment determines an ith prediction curve according to an ith offset distance in the N offset distances, the estimated longitudinal distance of the vehicle advancing, the determined transverse deviation of the position of the vehicle from the central line of the vehicle, the determined arc length value and the heading angle included in the state information of the vehicle; and i takes all positive integers not greater than N. And the electronic equipment selects one prediction curve from the determined N prediction curves as a target prediction curve. And finally, the electronic equipment determines the position of the vehicle after the vehicle runs along the target prediction curve at the running speed for a preset delay time by taking the position of the vehicle detected currently as a starting point.
In the embodiment of the invention, the predicted track is not based on the existing track or is not simply the central line reflecting the future trend of the vehicle as the predicted track line, but the state information of the vehicle and the geometric information of the central line of the running vehicle are obtained through the electronic equipment; then estimating a plurality of prediction curves through the acquired state information and geometric information of a center line of vehicle running; selecting one prediction curve from the determined N prediction curves as a target prediction curve; the current state of the vehicle and the possible future trend of the vehicle are combined to carry out scientific prediction to obtain a prediction curve, so that the correction precision of the delay positioning error is improved.
In one possible design, the electronic device estimates a longitudinal distance traveled by a vehicle based on a travel speed of the vehicle, including:
the electronic device estimates a longitudinal distance that the vehicle is heading by:
Sf＝kvv+Δsmin+Sd；
wherein, the SfRepresents the longitudinal distance, k, over which the vehicle is advancingvRepresenting the speed gain ratio, v representing the speed of travel of the vehicle, Δ sminDenotes the minimum steering distance offset value, SdIndicating a preset travel distance of the vehicle after traveling parallel to the center line.
When the longitudinal distance of the vehicle, which advances, is estimated, the longitudinal distance of the vehicle, which advances, is estimated according to the current state of the vehicle, and the accuracy of a predicted curve is improved, so that the correction accuracy of the delay positioning error is improved.
In one possible design, the ith prediction curve satisfies a cubic equation as shown in the following equation:
q(s)＝as3+bs2+cs+qi
the electronic equipment determines an ith prediction curve according to an ith offset distance in the N offset distances, the longitudinal distance of the vehicle, the heading angle, the arc length value and the transverse deviation of the position of the vehicle from the center line of the vehicle, and the ith prediction curve comprises the following steps:
the electronic equipment determines a, b and c in the cubic equation through conditions shown in the following formula so as to obtain the ith prediction curve:
Figure BDA0001149115480000031
Figure BDA0001149115480000032
Δs＝s-si；q(si)＝qi；
Figure BDA0001149115480000041
q(sf)＝qf；
Figure BDA0001149115480000042
wherein q(s) represents a cubic equation function, siRepresenting the arc length value; sfRepresenting a longitudinal distance of advancement of the vehicle; q. q.sfRepresents the ith offset distance; q. q.siA lateral deviation indicating that the vehicle is located at a position deviated from a center line on which the vehicle travels; θ represents the heading angle.
When the track is predicted, the vehicle running track is scientifically predicted by combining the state information of the vehicle and the geometric information of the running center line of the vehicle, so that a predicted curve is obtained, the accuracy of the predicted curve is improved, and the correction accuracy of the delay positioning error is improved.
In one possible design, the electronic device selects one prediction curve from the determined N prediction curves as a target prediction curve, including:
the electronic equipment determines the smoothness of each prediction curve in the N prediction curves through a smoothness function, and selects the prediction curve with the minimum smoothness as a target prediction curve; alternatively, the first and second electrodes may be,
the electronic equipment determines a path consistency value of each prediction curve in the N prediction curves through a path consistency function, and selects the prediction curve with the minimum path consistency value as a target prediction curve; alternatively, the first and second electrodes may be,
the electronic equipment determines smoothness of each of the N prediction curves through a smoothness function, determines a path consistency value of each of the N prediction curves through a path consistency function, and determines a prediction curve with the smallest weighted sum of the smoothness and the path consistency value as a target prediction curve.
And selecting one prediction curve from the determined N prediction curves as a target prediction curve through the weighted sum of the smoothness and the path consistency value, so that the accuracy of the vehicle track prediction curve is improved, and the correction accuracy of the delay positioning error is improved.
In one possible design, the obtaining the state information of the vehicle includes:
when the electronic equipment is a vehicle-mounted terminal of the vehicle, the electronic equipment acquires state information of the vehicle through a sensor arranged in the vehicle; alternatively, the first and second electrodes may be,
when the electronic equipment is a cloud server, the electronic equipment receives the state information of the vehicle, which is sent by a vehicle-mounted terminal of the vehicle.
In a second aspect, an embodiment of the present invention provides an apparatus for correcting a vehicle positioning error, including:
the acquisition module is used for acquiring the state information of the vehicle and the running center line of the vehicle; the state information comprises the position, the course angle and the running speed of the vehicle which are detected currently; the center line of the vehicle running is the center line of the preset length on the lane where the vehicle is located at present and from the position; the course angle is an included angle between the driving direction of the vehicle and the positive north direction;
the determining module is used for estimating the longitudinal distance of the vehicle advancing based on the driving speed of the vehicle acquired by the acquiring module, determining the transverse deviation of the position of the vehicle acquired by the acquiring module from the center line of the vehicle driving, and determining the arc length value of the closest point on the center line to the position of the vehicle;
the obtaining module is further configured to obtain N predetermined offset distances, where an ith offset distance is an estimated lateral deviation of an ith position of the vehicle from a center line where the vehicle runs, i is a positive integer not greater than N, and N is a positive integer greater than 1;
the prediction module is used for determining an ith prediction curve according to the ith offset distance in the N offset distances acquired by the acquisition module, the longitudinal distance of the vehicle advancing estimated by the determination module, the course angle acquired by the acquisition module, the arc length value determined by the determination module and the transverse deviation of the position of the vehicle from the center line of the vehicle, wherein the i-th prediction curve is taken by all positive integers not larger than N;
the selection module is used for selecting one prediction curve from the N prediction curves determined by the prediction module as a target prediction curve;
and the correction module is used for determining the position of the vehicle which is obtained by the obtaining module and is currently detected as the starting point, and the vehicle reaches the position after running along the target prediction curve determined by the selection module by the running speed obtained by the obtaining module for a preset delay time.
In one possible design, the determining module, when estimating the longitudinal distance traveled by the vehicle based on the traveling speed of the vehicle acquired by the acquiring module, is specifically configured to:
estimating a longitudinal distance traveled by the vehicle by:
Sf＝kvv+Δsmin+Sd；
wherein, the SfRepresents the longitudinal distance, k, over which the vehicle is advancingvRepresenting the speed gain ratio, v representing the speed of travel of the vehicle, Δ sminDenotes the minimum steering distance offset value, SdIndicating a preset travel distance of the vehicle after traveling parallel to the center line.
In one possible design, the i-th prediction curve determined by the prediction module satisfies a cubic equation as shown in the following formula:
q(s)＝as3+bs2+cs+qi
the prediction module is specifically configured to:
determining a, b and c in the cubic equation through the conditions shown in the following formula, thereby obtaining the ith prediction curve:
Figure BDA0001149115480000061
Figure BDA0001149115480000062
Δs＝s-si；q(si)＝qi；
Figure BDA0001149115480000063
q(sf)＝qf；
Figure BDA0001149115480000064
wherein q(s) represents a cubic equation function, siRepresenting the arc length value determined by the determination module; sfRepresenting a longitudinal distance of advancement of the vehicle estimated by the determination module; q. q.sfRepresenting the ith offset distance acquired by the acquisition module; q. q.siA lateral deviation representing a position at which the vehicle determined by the determination module is located from a center line on which the vehicle travels; theta represents the course angle acquired by the acquisition module.
In one possible design, the selection module is specifically configured to:
determining smoothness of each prediction curve in the N prediction curves determined by the prediction module through a smoothness function, and selecting the prediction curve with the minimum smoothness as a target prediction curve; alternatively, the first and second electrodes may be,
determining a path consistency value of each prediction curve in the N prediction curves determined by the prediction module through a path consistency function, and selecting the prediction curve with the minimum path consistency value as a target prediction curve; alternatively, the first and second electrodes may be,
and determining smoothness of each of the N prediction curves determined by the prediction module through a smoothness function, determining a path consistency value of each of the N prediction curves through a path consistency function, and determining the prediction curve with the smallest weighted sum of the smoothness and the path consistency value as a target prediction curve.
In one possible design, the obtaining module, when obtaining the state information of the obtained vehicle, is specifically configured to:
when the device is applied to a vehicle-mounted terminal of the vehicle, acquiring state information of the acquired vehicle through a sensor arranged in the vehicle; alternatively, the first and second electrodes may be,
when the device is applied to a cloud server, the state information of the vehicle, which is sent by a vehicle-mounted terminal of the vehicle, is received.
In a third aspect, an embodiment of the present invention further provides a vehicle positioning error correction device, including:
a processor and a memory.
The memory is used for storing program codes required to be executed by the processor. The processor is adapted to execute the program code stored in the memory, in particular to perform the method as set forth in the first aspect or any of the first aspect designs.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium for storing computer software instructions for executing the functions designed in any one of the first aspect and the first aspect, where the computer software instructions include a program designed to execute the method designed in any one of the first aspect and the first aspect.
Drawings
FIG. 1 is a flow chart of a method for correcting vehicle positioning errors according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a track cluster according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of estimating a longitudinal distance traveled by a vehicle according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of X-Y coordinate rectification provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating a principle of determining a path consistency value according to an embodiment of the present invention;
FIG. 6 is a schematic view of a vehicle positioning error correction apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a preferred implementation manner of a terminal according to an embodiment of the present invention;
FIG. 8 is a schematic illustration of a vehicle positioning error correction provided in accordance with an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a preferred implementation manner of a terminal according to an embodiment of the present invention;
FIG. 10 is a schematic structural diagram of a predicted target prediction curve according to an embodiment of the present invention;
fig. 11 is a schematic diagram of vehicle positioning error correction according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
The embodiment of the invention provides a vehicle positioning error correction method and device, which are used for solving the problem that the correction accuracy of a delayed positioning error is higher due to the fact that the difference between a predicted track curve and the actual trend of a vehicle is larger in the prior art. The method and the device are based on the same inventive concept, and because the principles of solving the problems of the method and the device are similar, the implementation of the device and the method can be mutually referred, and repeated parts are not repeated.
The following describes embodiments of the present invention in detail with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for correcting a vehicle positioning error according to an embodiment of the present invention is shown, where the method is executed by an electronic device, and the electronic device may be a cloud server or a vehicle-mounted terminal, and specifically includes the following steps:
s101, the electronic equipment acquires the state information of the vehicle and the running center line of the vehicle; the state information comprises the position, the course angle and the running speed of the vehicle which are detected currently; the center line of the vehicle running is the center line of the preset length on the lane where the vehicle is located at present and from the position; the course angle is an included angle between the driving direction of the vehicle and the positive north direction;
optionally, when the electronic device is a vehicle-mounted terminal of the vehicle, the electronic device may acquire the state information of the vehicle through a sensor provided in the vehicle.
Optionally, when the electronic device is a cloud server, the electronic device may receive the state information of the vehicle sent by the vehicle-mounted terminal of the vehicle. The vehicle is provided with a sensor, and a vehicle-mounted terminal of the vehicle acquires state information of the vehicle through the sensor of the vehicle and then sends the state information to the cloud server.
The center line of the vehicle running is obtained, and the center line of the vehicle running can be determined according to a high-precision map stored in the electronic device and the position where the vehicle is currently detected.
It should be noted that, in the embodiment of the present invention, both the position where the vehicle is currently detected and the center line where the vehicle runs are in the same xy space reference coordinate system. The acquired state information of the vehicle and the center line of travel of the vehicle are then converted into a coordinate system having an arc length value s as an abscissa and an offset center line distance q as an ordinate according to a cubic spline function.
Specifically, the corresponding values of each point on the center line, the position of the vehicle currently detected and the heading angle in the s-q coordinate system are determined according to the following cubic spline interpolation function:
taking M points on the central line, carrying out cubic spline interpolation to obtain M-1 functions, and obtaining the coefficient value of each function through calculation, wherein the relation between the X-Y coordinate corresponding to the jth function and the S satisfies the following functional relation:
Figure BDA0001149115480000091
wherein, the xm(s) represents a function of X coordinate to s, ax,jFor a preset cubic parameter for the X coordinate, bx,jFor a predetermined quadratic parameter for the X coordinate, cx,jFor a preset first power parameter for the X coordinate, dx,jIs a constant parameter; said ym(s) a function for converting the Y coordinate into s, ay,jIs a preset cubic parameter for the Y coordinate, by,jFor a preset quadratic parameter for the Y coordinate, cy,jFor a preset first power parameter for the Y coordinate, dy,jAnd Sj represents the arc length value of the j section on the central line as a constant parameter.
S102, the electronic equipment estimates the advancing longitudinal distance of the vehicle based on the running speed of the vehicle, determines the transverse deviation of the position of the vehicle from the running center line of the vehicle, and determines the arc length value of the closest point on the center line to the position of the vehicle.
The longitudinal distance that the vehicle is advanced is the maximum forward predicted distance of the vehicle to be corrected.
S103, the electronic equipment acquires N preset offset distances, wherein the ith offset distance is the estimated transverse deviation of the ith position of the vehicle from the driving center line of the vehicle, i is a positive integer not larger than N, and N is a positive integer larger than 1.
S104, the electronic equipment determines an ith prediction curve according to the ith offset distance in the N offset distances, the forward longitudinal distance of the vehicle, the heading angle, the arc length value and the transverse deviation of the position of the vehicle from the driving center line of the vehicle, wherein i is taken over all positive integers not greater than N.
In the embodiment of the present invention, N prediction curves are determined after step S104, so that a track cluster is formed, where the track cluster is a set of N prediction curves. Wherein if a distance between the ith offset distance and the (i + 1) th offset distance is r, the r is a positive real number greater than 0 and less than a lateral deviation of a position where the vehicle is located from a center line where the vehicle is running. Preferably, the value of r may be 0.5, as shown in fig. 2, which is a schematic diagram of a track cluster, where a horizontal axis is an X axis and a vertical axis is a Y axis.
In addition, it should be noted that, in each prediction curve determined in the embodiment of the present invention, the longitudinal distance that the vehicle advances and the lateral deviation that the vehicle is located at from the center line where the vehicle travels are the same.
Suppose that the ith pre-estimated curve is as shown in FIG. 3, qfDenotes the ith offset distance, SiRepresenting said arc length value, qiRepresents a lateral deviation of the position of the vehicle from a center line of travel of the vehicle, theta represents the course angle, SfRepresenting the longitudinal distance travelled by the vehicle. The heading angle refers to an included angle between the driving direction of the vehicle and the true north direction. By configuring q differentlyfThereby obtainingTo different prediction curves.
S105, the electronic equipment selects one prediction curve from the determined N prediction curves as a target prediction curve.
S106, the electronic equipment determines the position reached by the vehicle after the vehicle runs along the target prediction curve at the running speed for a preset delay time by taking the position of the vehicle detected currently as a starting point.
In the embodiment of the invention, the predicted track is not based on the existing track or is not simply the central line reflecting the future trend of the vehicle as the predicted track line, but the state information of the vehicle and the geometric information of the central line of the running vehicle are obtained through the electronic equipment; then estimating a plurality of prediction curves through the acquired state information and geometric information of a center line of vehicle running; selecting one prediction curve from the determined N prediction curves as a target prediction curve; the current state of the vehicle and the possible future trend of the vehicle are combined to carry out scientific prediction to obtain a prediction curve, so that the correction precision of the delay positioning error is improved.
In one possible implementation, the electronic device estimates the longitudinal distance that the vehicle advances based on the traveling speed of the vehicle, and may be implemented as follows:
the electronic device estimates a longitudinal distance that the vehicle is heading by:
Sf＝kvv+Δsmin+Sd(ii) a Formula (1.2)
Wherein, the SfRepresents the longitudinal distance, k, over which the vehicle is advancingvRepresenting the speed gain ratio, v representing the speed of travel of the vehicle, Δ sminDenotes the minimum steering distance offset value, SdIndicating a preset travel distance of the vehicle after traveling the parallel center line. The speed gain proportion and the preset running distance of the vehicle after running parallel to the center line can be set to different values according to different vehicle types. Vehicle types include cars, trucks, vans, and the like.
When the longitudinal distance of the vehicle in the advancing process is estimated, the longitudinal distance of the vehicle in the advancing process is estimated by combining the current state of the vehicle without taking the center line reflecting the future trend of the vehicle as a prediction basis according to the existing track or the center line, so that the accuracy of a prediction curve is improved, and the correction accuracy of the delay positioning error is improved.
Optionally, each of the N prediction curves satisfies a cubic equation shown by the following formula:
q(s)＝as3+bs2+cs+qiformula (1.3)
The electronic equipment determines an ith prediction curve according to an ith offset distance in the N offset distances, the longitudinal distance of the vehicle, the heading angle, the arc length value and the transverse deviation of the position of the vehicle from the center line of the vehicle, and the ith prediction curve comprises the following steps:
the electronic device determines a, b and c in the cubic equation through the conditions shown in the following formula (1.4) and formula (1.5), so as to obtain the ith prediction curve:
Figure BDA0001149115480000111
Figure BDA0001149115480000121
Δs＝s-si；q(si)＝qi；
Figure BDA0001149115480000122
q(sf)＝qf；
Figure BDA0001149115480000123
wherein q(s) represents a cubic equation function, siRepresenting the arc length value; sfRepresenting a longitudinal distance of advancement of the vehicle; q. q.sfRepresents the ith offset distance; q. q.siA lateral deviation indicating that the vehicle is located at a position deviated from a center line on which the vehicle travels; θ represents the heading angle; s meterFunction variable, arc length value.
When the track is predicted, the vehicle running track is scientifically predicted by combining the state information of the vehicle and the geometric information of the running center line of the vehicle, so that a predicted curve is obtained, the accuracy of the predicted curve is improved, and the correction accuracy of the delay positioning error is improved.
Each prediction curve obtained in the above way takes the arc length value as an independent variable and q-s as a function of a coordinate system. Therefore, after obtaining the N prediction curves, when visualization management is needed, each curve needs to be converted into the X and Y coordinate axes. Specifically, the electronic device collects M sampling points and obtains an arc length value s of the mth sampling point within a longitudinal distance range in which the vehicle advances on the ith prediction curve, wherein a sampling interval is d, d is a positive real number greater than 0 and smaller than the longitudinal distance in which the vehicle advances, a preferred value of d is 0.5, M is the number of sampling points and is a positive integer greater than or equal to 1, and M is all positive integers not greater than M.
The arc length value s of the mth sampling point is obtained to calculate the X, Y coordinate of the corresponding mth sampling point, and specifically, the arc length value s and the X-Y coordinate corresponding to each sampling point can be calculated by a formula (1.1).
The xy coordinates obtained by the formula (1.1) are coordinates when the vehicle has traveled the arc length value s, but each predicted curve has a lateral deviation, so the actual position of the vehicle also needs to take into account the influence of the lateral deviation of the vehicle offset through the curve calculated by the formula (1.1), as shown in fig. 4, for example.
And converting the X and Y coordinates of the actual position corresponding to the sampling point as follows, wherein the converted sampling point conforms to the following formula (1.6):
Figure BDA0001149115480000124
q represents the lateral deviation of the curve calculated by the formula (1.1) of the offset corresponding to the sampling point, and α is obtained by inverse tangent operation of the corresponding slope of each point on the centerline.
The value of each prediction curve in the X-Y coordinate system is obtained through the calculation, so that each prediction curve can be visualized.
In addition, when it needs to be described, before converting the intermediate quantity to obtain the coordinates of the sampling point X and Y, it is determined whether the position of the vehicle is located above or below the center line, and the specific determination method includes: taking n points on the central line, sequentially connecting the n points to form a polygon, judging whether the vehicle is in the polygon, and if the vehicle is in the polygon, determining that the vehicle is above the central line; if the vehicle is determined to be outside the polygon, determining that the vehicle is below the centerline. The judging whether the vehicle is inside the polygon may be performed by an area sum judging method, an included angle sum judging method, an injection line method, a cross multiplication judging method, and the like, and the method is not particularly limited herein.
When the vehicle is determined to be above the centerline, x is calculated in calculating the X, Y coordinates of the sample pointm(s)-q×sinα＝X，ym(s) + q × cos α ═ Y, x when the vehicle is determined to be below the centerlinem(s)+q×sinα＝X；ym(s)-q×cosα＝Y。
Optionally, when the electronic device selects one prediction curve from the determined N prediction curves as the target prediction curve, the method may be implemented in any one of the following manners:
the first implementation mode comprises the following steps:
and the electronic equipment determines the smoothness of each prediction curve in the N prediction curves through a smoothness function, and selects the prediction curve with the minimum smoothness as a target prediction curve.
The second implementation mode comprises the following steps:
and the electronic equipment determines the path consistency value of each prediction curve in the N prediction curves through a path consistency function, and selects the prediction curve with the minimum path consistency value as a target prediction curve.
The third implementation mode comprises the following steps:
the electronic equipment determines smoothness of each of the N prediction curves through a smoothness function, determines a path consistency value of each of the N prediction curves through a path consistency function, and determines a prediction curve with the smallest weighted sum of the smoothness and the path consistency value as a target prediction curve.
Specifically, the electronic device determines smoothness of each of the N prediction curves through a smoothness function, and calculates the smoothness of each prediction curve through the smoothness function, that is, by integrating the curvature values on each prediction curve. The smoothness function is specifically as follows:
CK[i]＝∫ki 2(s)dsp＝∫ki 2(s) Q(s) ds formula (1.7)
Wherein, CK[i]Representing the smoothness value, k, of the prediction curvei(s) represents a curvature value corresponding to the differential arc segment; s represents the arc length value, and Q(s) represents the transformation function of the curvature value and the arc length value.
Specifically, the electronic device determines a path consistency value of each of the N prediction curves through a path consistency function, where the path consistency function specifically includes:
Figure BDA0001149115480000141
wherein, CC[i]A path consistency value, s, representing the ith prediction curve2Indicates the end point of the ith prediction curve, s1Denotes the starting point of the ith prediction curve, liRepresenting the euclidean distance of the overlapping parts of the ith prediction curve. The overlap portion is an overlap portion of the current target prediction curve and the portion where the vehicle is not traveling in the target prediction curve obtained last time. Fig. 5 is a schematic diagram for determining a path consistency value.
And selecting one prediction curve from the determined N prediction curves as a target prediction curve through the weighted sum of the smoothness and the path consistency value, so that the accuracy of the vehicle track prediction curve is improved, and the correction accuracy of the delay positioning error is improved.
Based on the same inventive concept as the method embodiment corresponding to fig. 1, the embodiment of the present invention provides a vehicle positioning error correction apparatus 10, where the apparatus 10 may be applied to a cloud server, and may also be applied to a vehicle-mounted terminal of a vehicle. The apparatus 10 is structured as shown in fig. 6, and includes an obtaining module 11, a determining module 12, a predicting module 13, a selecting module 14, and a correcting module 15, wherein:
the acquiring module 11 is used for acquiring the state information of the vehicle and the center line of the vehicle running; the state information comprises the position, the course angle and the running speed of the vehicle which are detected currently; the center line of the vehicle running is the center line of the preset length on the lane where the vehicle is located at present and from the position; the course angle is an included angle between the driving direction of the vehicle and the positive north direction.
The determining module 12 is configured to estimate a longitudinal distance that the vehicle advances based on the driving speed of the vehicle acquired by the acquiring module 11, determine a lateral deviation that the position where the vehicle is located, acquired by the acquiring module 11, deviates from the center line where the vehicle is driving, acquired by the acquiring module 11, and determine an arc length value of a closest point on the center line from the position where the vehicle is located.
The obtaining module 11 is further configured to obtain N predetermined offset distances, where an ith offset distance is an estimated lateral deviation of an ith position of the vehicle from a center line where the vehicle runs, i is a positive integer not greater than N, and N is a positive integer greater than 1.
The prediction module 13 is configured to determine an ith prediction curve according to an ith offset distance of the N offset distances acquired by the acquisition module 11, the longitudinal distance of the vehicle advancing estimated by the determination module 12, the heading angle acquired by the acquisition module 11, the arc length value determined by the determination module 12, and a lateral deviation of the position of the vehicle from a center line where the vehicle travels, where i is taken as all positive integers not greater than N.
A selecting module 14, configured to select one prediction curve from the N prediction curves determined by the predicting module 13 as a target prediction curve.
A correcting module 15, configured to determine, using the current detected position of the vehicle acquired by the acquiring module 11 as a starting point, a position where the vehicle arrives after traveling along the target prediction curve determined by the selecting module 14 for a predetermined delay time at the traveling speed acquired by the acquiring module 11.
Optionally, when the determining module 12 estimates the longitudinal distance that the vehicle advances based on the driving speed of the vehicle acquired by the acquiring module 11, specifically, the determining module is configured to:
estimating a longitudinal distance traveled by the vehicle by:
Sf＝kvv+Δsmin+Sd(ii) a Formula (2.1)
Wherein, the SfRepresents the longitudinal distance, k, over which the vehicle is advancingvRepresenting the speed gain ratio, v representing the speed of travel of the vehicle, Δ sminDenotes the minimum steering distance offset value, SdIndicating a preset travel distance of the vehicle after traveling parallel to the center line.
Optionally, the ith prediction curve determined by the prediction module 13 satisfies a cubic equation shown in the following formula:
q(s)＝as3+bs2+cs+qiformula (2.2)
The prediction module 13 is specifically configured to:
determining a, b and c in the cubic equation through the conditions shown in the following formula, thereby obtaining the ith prediction curve:
Figure BDA0001149115480000161
Figure BDA0001149115480000162
Δs＝s-si；q(si)＝qi；
Figure BDA0001149115480000163
q(sf)＝qf；
Figure BDA0001149115480000164
wherein q(s) represents a cubic equation function, siRepresents the arc length value determined by the determination module 12; sfRepresents the longitudinal distance the vehicle is advancing as estimated by the determination module 12; q. q.sfRepresents the ith offset distance acquired by the acquisition module 12; q. q.siA lateral deviation representing a position at which the vehicle determined by the determination module 12 is located from a center line on which the vehicle travels; θ represents the heading angle acquired by the acquisition module 11.
Optionally, the selecting module 14 is specifically configured to:
determining the smoothness of each of the N prediction curves determined by the prediction module 13 through a smoothness function, and selecting the prediction curve with the smallest smoothness as a target prediction curve; alternatively, the first and second electrodes may be,
determining a path consistency value of each of the N prediction curves determined by the prediction module 13 through a path consistency function, and selecting a prediction curve with the smallest path consistency value as a target prediction curve; alternatively, the first and second electrodes may be,
the smoothness of each of the N prediction curves determined by the prediction module 13 is determined by a smoothness function, the path consistency value of each of the N prediction curves is determined by a path consistency function, and the prediction curve with the smallest weighted sum of the smoothness and the path consistency value is determined as a target prediction curve.
Optionally, when the apparatus 10 is applied to a vehicle-mounted terminal of the vehicle, the obtaining module 11, when obtaining the state information of the obtained vehicle, is specifically configured to:
and acquiring the state information of the acquired vehicle through a sensor arranged in the vehicle.
Optionally, when the apparatus 10 is applied to a cloud server, the obtaining module 11 is specifically configured to, when obtaining the state information of the vehicle:
and acquiring the state information of the acquired vehicle by receiving the state information of the vehicle sent by the vehicle-mounted terminal of the vehicle.
The division of the modules in the embodiments of the present application is schematic, and only one logical function division is provided, and in actual implementation, there may be another division manner, and in addition, each functional module in each embodiment of the present application may be integrated in one processor, may also exist alone physically, or may also be integrated in one module by two or more modules. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
When the vehicle positioning error correction apparatus is applied to an On Board Unit (OBU) of a vehicle, the integrated module may be implemented in a form of hardware, as shown in fig. 7, and may include a sensor 1201, a processor 1202 and a memory 1203, and optionally, a wireless transceiver 1204 for transceiving a wireless signal. The physical hardware corresponding to the obtaining module 11, the determining module 12, the predicting module 13, the selecting module 14 and the correcting module 15 may be the processor 1202. The processor 1202 may be a Central Processing Unit (CPU), a digital processing unit, or the like. The processor 1202 acquires the state information of the vehicle through the sensor 1201. A memory 1203 for storing programs executed by the processor 1202.
The specific connection medium among the sensor 1201, the processor 1202, the memory 1203, and the wireless transceiver 1204 is not limited in the embodiments of the present application. In fig. 7, the memory 1203, the processor 1202, the sensor 1201 and the wireless transceiver 1204 are connected by a bus 1205, the bus is indicated by a thick line in fig. 7, and the connection manner between the other components is only schematically illustrated and is not limited. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.
The memory 1203 may be a volatile memory (volatile memory), such as a random-access memory (RAM); the memory 1203 may also be a non-volatile memory (non-volatile memory), such as a read-only memory (ROM), a flash memory (flash memory), a Hard Disk Drive (HDD) or a solid-state drive (SSD), or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to this. The memory 1203 may be a combination of the above.
The processor 1202 is configured to execute the program code stored in the memory 1203, and is specifically configured to execute the method described in the embodiment corresponding to fig. 1 to fig. 5, which may be specifically implemented with reference to the embodiment corresponding to fig. 1 to fig. 5, and details of the method are not repeated herein.
The following gives a specific application scenario of the vehicle positioning error correction device applied to a vehicle-mounted terminal of a vehicle, and further details are given for a process of vehicle positioning error correction:
the vehicle needing to correct the positioning error is provided with a high-precision map besides an OBU. In the embodiment of the invention, a plurality of sensors are arranged in the vehicle, for example, a sensor capable of realizing a high-precision Global Positioning System (GPS) + carrier-phase differential technology (RTK), a Micro Electro Mechanical System (MEMS), and the like.
A1, the OBU collects vehicle status information through a plurality of sensors.
A2, the OBU acquires a high-precision map configured in advance, and determines the center line of the vehicle according to the high-precision map.
A3, after determining the center line of the lane where the vehicle is traveling and the state information of the current vehicle, the OBU determines a predicted curve of the vehicle according to the center line of the lane where the vehicle is traveling and the state information of the current vehicle, where the predicted curve of the vehicle may be determined by the method described in the embodiment corresponding to fig. 1, and details of the embodiment of the present invention are not repeated here.
A4, after determining the target prediction curve, the OBU estimates the actual position of the vehicle travelling according to the target prediction curve according to the current detected position of the vehicle, the preset delay time and the running speed of the vehicle, and realizes the automatic control of the vehicle according to the actual position. The actual position of the vehicle is determined after the correction, for example, as shown in fig. 8.
When the vehicle positioning error correction apparatus is applied to a cloud server, the integrated module may be implemented in the form of hardware, as shown in fig. 9, and may include a wireless transceiver 1301, a processor 1302, and a memory 1303. The physical hardware corresponding to the obtaining module 11, the determining module 12, the predicting module 13, the selecting module 14 and the correcting module 15 may be the processor 1302. The processor 1302 may be a CPU, a digital processing unit, or the like. The processor 1302 receives the state information of the vehicle transmitted from the in-vehicle terminal of the vehicle through the wireless transceiver 1301. And a memory 1303 for storing programs executed by the processor 1302.
In the embodiment of the present application, a specific connection medium among the wireless transceiver 1301, the processor 1302, and the memory 1303 is not limited. In the embodiment of the present application, the memory 1303, the processor 1302, and the sensor 1301 are connected by a bus 1305 in fig. 9, the bus is represented by a thick line in fig. 9, and the connection manner between other components is merely illustrative and not limited. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 9, but this does not indicate only one bus or one type of bus.
The memory 1303 may be a volatile memory, such as RAM; the memory 1303 may also be a nonvolatile memory such as a ROM, a flash memory, an HDD or an SSD, or the memory 1303 is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto. The memory 1303 may be a combination of the above memories.
The processor 1302 is configured to execute the program code stored in the memory 1303, and is specifically configured to execute the method described in the embodiment corresponding to fig. 1 to 5, which may be specifically implemented with reference to the embodiment corresponding to fig. 1 to 5, and is not described herein again.
The following provides a specific application scenario of the vehicle positioning error correction device applied to the cloud server, and further details are given for a vehicle positioning error correction process:
the vehicle track prediction system comprises the cloud server. The vehicle track prediction system comprises a cloud server and a vehicle, and the vehicle comprises a wireless transceiver.
And the cloud server is used for receiving the vehicle state information sent by the vehicle-mounted terminal of the vehicle through the wireless transceiver and determining the prediction curve.
And the wireless transceiver is used for realizing information transmission between the vehicle and the cloud server.
The vehicle can further comprise an OBU, and the OBU is used for automatically controlling the vehicle according to the prediction curve, measuring the vehicle state information in real time and returning the vehicle state information to the wireless transceiver, so that the wireless transceiver sends the vehicle state information to the cloud server.
In the embodiment of the invention, a high-precision map can be configured in the cloud server, so that basic data support is provided for the cloud server to determine the vehicle track prediction curve, and the basic data mainly comprises map information, geometric information of the center line of a lane, the number of lanes, road width, lane speed limit and other information:
in the embodiment of the invention, a plurality of sensors are arranged in the vehicle, such as a sensor capable of realizing GPS + RTK, MEMS inertial navigation and the like.
B1, the OBU acquires the vehicle state information through a plurality of sensors; the vehicle status information is then transmitted to the wireless transceiver.
Specifically, in order to improve the transmission speed and reduce the resource occupation, the OBU may compress the acquired vehicle state to obtain a compressed packet, and then transmit the compressed packet to the wireless transceiver.
And B2, after receiving the state information of the vehicle, the wireless transceiver transmits the vehicle state information to a cloud server.
And B3, the cloud server receives the state information of the vehicle transmitted by the wireless transceiver, acquires a pre-configured high-precision map, and determines the driving center line of the vehicle according to the high-precision map.
And if the cloud server receives the compressed packet sent by the wireless transceiver, decompressing to obtain the state information of the vehicle after receiving the compressed packet.
B4, after acquiring the center line of the lane where the vehicle is traveling and the state information of the current vehicle, the cloud server determines a predicted curve of the vehicle according to the center line of the lane where the vehicle is traveling and the state information of the current vehicle, where the predicted curve of the vehicle may be determined by the method described in the embodiment corresponding to fig. 1, and details of the embodiment of the present invention are not repeated herein. The predicted prediction curve can be seen in fig. 10, where k(s) represents the predicted prediction curve in a coordinate system with q as the Y axis and s as the X axis. Kb(s) is the center line of travel of the vehicle.
And B5, after the cloud server determines the target prediction curve, estimating the actual position of the vehicle travelling according to the target prediction curve according to the currently detected position of the vehicle, the preset delay time and the running speed of the vehicle, and sending the actual position to the OBU through the wireless transceiver. Thus, the OBU realizes automatic control of the vehicle. The actual position of the vehicle is determined after the correction, for example, as shown in fig. 11.
The preferred embodiments described herein are only for illustrating and explaining the present invention and are not intended to limit the present invention, and the embodiments and functional blocks in the embodiments may be combined with each other in the present application without conflict.
In the embodiment of the invention, the predicted track is not based on the existing track or is not simply the central line reflecting the future trend of the vehicle as the predicted track line, but the state information of the vehicle and the geometric information of the central line of the running vehicle are obtained through the electronic equipment; then estimating a plurality of prediction curves through the acquired state information and geometric information of a center line of vehicle running; selecting one prediction curve from the determined N prediction curves as a target prediction curve; the current state of the vehicle and the possible future trend of the vehicle are combined to carry out scientific prediction to obtain a prediction curve, so that the correction precision of the delay positioning error is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A method for correcting a vehicle positioning error, comprising:
the method comprises the steps that the electronic equipment obtains state information of a vehicle and a center line of vehicle running; the state information comprises the position, the course angle and the running speed of the vehicle which are detected currently; the center line of the vehicle running is the center line of the preset length on the lane where the vehicle is located at present and from the position; the course angle is an included angle between the driving direction of the vehicle and the positive north direction;
the electronic equipment estimates the longitudinal distance of the vehicle advancing based on the running speed of the vehicle, determines the transverse deviation of the position of the vehicle from the center line of the vehicle running, and determines the arc length value of the closest point on the center line to the position of the vehicle;
the electronic equipment acquires N predetermined offset distances, wherein the ith offset distance is the estimated transverse deviation of the ith position of the vehicle from the driving center line of the vehicle, i is a positive integer not greater than N, and N is a positive integer greater than 1;
the electronic equipment determines an ith prediction curve according to an ith offset distance in the N offset distances, the forward longitudinal distance of the vehicle, the course angle, the arc length value and the transverse deviation of the position of the vehicle from the center line of the vehicle, wherein i is taken over all positive integers not greater than N;
the electronic equipment selects one prediction curve from the determined N prediction curves as a target prediction curve;
and the electronic equipment determines the position reached by the vehicle after the vehicle runs along the target prediction curve at the running speed for a preset delay time by taking the position of the vehicle detected currently as a starting point.
2. The method of claim 1, wherein the electronic device estimates a longitudinal distance traveled by a vehicle based on a travel speed of the vehicle, comprising:
the electronic device estimates a longitudinal distance that the vehicle is heading by:
Sf＝kvv+Δsmin+Sd；
wherein, the SfRepresents the longitudinal distance, k, over which the vehicle is advancingvRepresenting the speed gain ratio, v representing the speed of travel of the vehicle, Δ sminDenotes the minimum steering distance offset value, SdIndicating a preset travel distance of the vehicle after traveling parallel to the center line.
3. The method of claim 1, wherein the ith prediction curve satisfies a cubic equation as shown in the following formula:
q(s)＝as3+bs2+cs+qi
the electronic equipment determines an ith prediction curve according to an ith offset distance in the N offset distances, the longitudinal distance of the vehicle, the heading angle, the arc length value and the transverse deviation of the position of the vehicle from the center line of the vehicle, and the ith prediction curve comprises the following steps:
the electronic equipment determines a, b and c in the cubic equation through conditions shown in the following formula so as to obtain the ith prediction curve:
Figure FDA0002408972700000021
Figure FDA0002408972700000022
Δs＝s-si；q(si)＝qi；
Figure FDA0002408972700000023
q(sf)＝qf；
Figure FDA0002408972700000024
wherein q(s) represents a cubic equation function, siAn arc length value representing a closest point on the centerline to a location where the vehicle is located; sfRepresenting a longitudinal distance of advancement of the vehicle; q. q.sfRepresents the ith offset distance; q. q.siA lateral deviation indicating that the vehicle is located at a position deviated from a center line on which the vehicle travels; θ represents the heading angle.
4. The method of any of claims 1 to 3, wherein the electronic device selects one prediction curve from the determined N prediction curves as a target prediction curve, comprising:
the electronic equipment determines the smoothness of each prediction curve in the N prediction curves through a smoothness function, and selects the prediction curve with the minimum smoothness as a target prediction curve; alternatively, the first and second electrodes may be,
the electronic equipment determines a path consistency value of each prediction curve in the N prediction curves through a path consistency function, and selects the prediction curve with the minimum path consistency value as a target prediction curve; alternatively, the first and second electrodes may be,
the electronic equipment determines smoothness of each of the N prediction curves through a smoothness function, determines a path consistency value of each of the N prediction curves through a path consistency function, and determines a prediction curve with the smallest weighted sum of the smoothness and the path consistency value as a target prediction curve.
5. The method of any one of claims 1 to 3, wherein the obtaining of the status information of the vehicle comprises:
when the electronic equipment is a vehicle-mounted terminal of the vehicle, the electronic equipment acquires state information of the vehicle through a sensor arranged in the vehicle; alternatively, the first and second electrodes may be,
when the electronic equipment is a cloud server, the electronic equipment receives the state information of the vehicle, which is sent by a vehicle-mounted terminal of the vehicle.
6. A vehicle positioning error correction apparatus, comprising:
the acquisition module is used for acquiring the state information of the vehicle and the running center line of the vehicle; the state information comprises the position, the course angle and the running speed of the vehicle which are detected currently; the center line of the vehicle running is the center line of the preset length on the lane where the vehicle is located at present and from the position; the course angle is an included angle between the driving direction of the vehicle and the positive north direction;
the determining module is used for estimating the longitudinal distance of the vehicle advancing based on the driving speed of the vehicle acquired by the acquiring module, determining the transverse deviation of the position of the vehicle acquired by the acquiring module from the center line of the vehicle driving acquired by the acquiring module, and determining the arc length value of the closest point on the center line to the position of the vehicle;
the obtaining module is further configured to obtain N predetermined offset distances, where an ith offset distance is an estimated lateral deviation of an ith position of the vehicle from a center line where the vehicle runs, i is a positive integer not greater than N, and N is a positive integer greater than 1;
the prediction module is used for determining an ith prediction curve according to the ith offset distance in the N offset distances acquired by the acquisition module, the longitudinal distance of the vehicle advancing estimated by the determination module, the course angle acquired by the acquisition module, the arc length value determined by the determination module and the transverse deviation of the position of the vehicle from the center line of the vehicle, wherein the i-th prediction curve is taken by all positive integers not larger than N;
the selection module is used for selecting one prediction curve from the N prediction curves determined by the prediction module as a target prediction curve;
and the correction module is used for determining the position of the vehicle which is obtained by the obtaining module and is currently detected as the starting point, and the vehicle reaches the position after running along the target prediction curve determined by the selection module by the running speed obtained by the obtaining module for a preset delay time.
7. The apparatus according to claim 6, wherein the determining module, when estimating the longitudinal distance travelled by the vehicle based on the speed of travel of the vehicle acquired by the acquiring module, is specifically configured to:
estimating a longitudinal distance traveled by the vehicle by:
Sf＝kvv+Δsmin+Sd；
wherein, the SfRepresents the longitudinal distance, k, over which the vehicle is advancingvRepresenting the speed gain ratio, v representing the speed of travel of the vehicle, Δ sminDenotes the minimum steering distance offset value, SdIndicating a preset travel distance of the vehicle after traveling parallel to the center line.
8. The apparatus of claim 6, wherein the i-th prediction curve determined by the prediction module satisfies a cubic equation as shown in the following formula:
q(s)＝as3+bs2+cs+qi
the prediction module is specifically configured to:
determining a, b and c in the cubic equation through the conditions shown in the following formula, thereby obtaining the ith prediction curve:
Figure FDA0002408972700000041
Figure FDA0002408972700000042
Δs＝s-si；q(si)＝qi；
Figure FDA0002408972700000043
q(sf)＝qf；
Figure FDA0002408972700000044
wherein q(s) represents a cubic equation function, siRepresenting the arc length value determined by the determination module; sfRepresenting a longitudinal distance of advancement of the vehicle estimated by the determination module; q. q.sfRepresenting the ith offset distance acquired by the acquisition module; q. q.siA lateral deviation representing a position at which the vehicle determined by the determination module is located from a center line on which the vehicle travels; theta represents the course angle acquired by the acquisition module.
9. The apparatus according to any one of claims 6 to 8, wherein the selection module is specifically configured to:
determining smoothness of each prediction curve in the N prediction curves determined by the prediction module through a smoothness function, and selecting the prediction curve with the minimum smoothness as a target prediction curve; alternatively, the first and second electrodes may be,
determining a path consistency value of each prediction curve in the N prediction curves determined by the prediction module through a path consistency function, and selecting the prediction curve with the minimum path consistency value as a target prediction curve; alternatively, the first and second electrodes may be,
and determining smoothness of each of the N prediction curves determined by the prediction module through a smoothness function, determining a path consistency value of each of the N prediction curves through a path consistency function, and determining the prediction curve with the smallest weighted sum of the smoothness and the path consistency value as a target prediction curve.
10. The apparatus according to any one of claims 6 to 8, wherein the obtaining module, when obtaining the status information of the vehicle, is specifically configured to:
when the device is applied to a vehicle-mounted terminal of the vehicle, acquiring state information of the acquired vehicle through a sensor arranged in the vehicle; alternatively, the first and second electrodes may be,
when the device is applied to a cloud server, the state information of the vehicle, which is sent by a vehicle-mounted terminal of the vehicle, is received.
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